BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings

TL;DR

This paper introduces BRAIN2DEPTH, a lightweight CNN model that efficiently classifies cognitive states from EEG data, achieving high performance with significantly fewer parameters suitable for real-time neurofeedback applications.

Contribution

The paper presents a novel, simple CNN architecture with fewer than 4% of the parameters of existing models, optimized for real-time EEG cognitive state classification.

Findings

Achieved comparable classification accuracy with fewer parameters.

Designed a 4-block CNN with depth-wise and separable convolutions.

Demonstrated suitability for real-time neurofeedback environments.

Abstract

Several Convolutional Deep Learning models have been proposed to classify the cognitive states utilizing several neuro-imaging domains. These models have achieved significant results, but they are heavily designed with millions of parameters, which increases train and test time, making the model complex and less suitable for real-time analysis. This paper proposes a simple, lightweight CNN model to classify cognitive states from Electroencephalograph (EEG) recordings. We develop a novel pipeline to learn distinct cognitive representation consisting of two stages. The first stage is to generate the 2D spectral images from neural time series signals in a particular frequency band. Images are generated to preserve the relationship between the neighboring electrodes and the spectral property of the cognitive events. The second is to develop a time-efficient, computationally less loaded, and high-performing model. We design a network containing 4 blocks and major components include standard and depth-wise convolution for increasing the performance and followed by separable convolution to decrease the number of parameters which maintains the tradeoff between time and performance. We experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. We compare performance with six commonly used machine learning classifiers and four state of the art deep learning models. We attain comparable performance utilizing less than 4\% of the parameters of other models. This model can be employed in a real-time computation environment such as neurofeedback.